[Eeglablist] Removing bad channels before average referencing - how stringent should I be?

[Jenny Bress]

Thanks to Makoto, Tarik, and Dheeraj for all of your helpful suggestions --
this gives me a much better sense of what to do.

On Thu, Aug 25, 2016 at 9:23 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> Dear Jenny,
>
> For you, I gave major update on this wiki page.
> https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline
> See the steps 6-9.
>
> > but I'm not sure how stringent to be.
>
> See Nima's PREP paper (Bigdely-Shamlo et al., 2015). There is a figure
> that illustrates what happens when you include bad channel to average
> reference.
>
> > Clearly I would remove channels recorded from electrodes with a
> consistently bad connection, but what about channels that show slight 60 Hz
> interference, or channels that were poorly connected for the first 20% of
> the trials, but were then fixed and look fine for the rest of the task?
>
> If 10% of datapoint rejection cannot clean the data, give up the channel.
> That's the rule of thumb. See Delorme et al. (2007) NeuroImage.
>
> However, I recommend you use clean_rawdata(). This has ASR, artifact
> subspace reconstruction, which is a very smart algorithm.
> http://sccn.ucsd.edu/eeglab/plugins/ASR.pdf
>
> Makoto
>
> On Wed, Aug 24, 2016 at 8:35 AM, Jenny Bress <jennifer.bress at gmail.com>
> wrote:
>
>> I recently began working in a lab that uses a dense-array cap and average
>> referencing. I typically work with a smaller number of electrodes and use
>> mastoid referencing, so this is a bit new to me. I know I should be
>> removing bad channels before re-referencing to the average in order to
>> avoid noise propagating to other channels, but I'm not sure how stringent
>> to be. Clearly I would remove channels recorded from electrodes with a
>> consistently bad connection, but what about channels that show slight 60 Hz
>> interference, or channels that were poorly connected for the first 20% of
>> the trials, but were then fixed and look fine for the rest of the task? Any
>> advice would be much appreciated.
>>
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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